Data Warehouses, Lakes, Lakehouses and Hubs: Great for Analytics — But Not Built for Real Time

Enterprises today face challenges in meeting real-time data demands due to the limitations of existing data architectures, which were primarily built for batch analytics rather than real-time experiences. While data warehouses, lakes, and the emerging lakehouses offer structured analytics, flexible storage, and unified capabilities respectively, they fall short in providing real-time responsiveness, necessary for modern digital experiences and AI systems. Gartner's report suggests combining these architectures for varied analytics needs, yet it overlooks the critical aspect of real-time performance, including streaming data ingestion and low-latency querying. SingleStore offers a solution by acting as a performance layer that complements existing architectures like Snowflake and Databricks, enabling real-time queries and AI-driven applications without compromising scalability. This approach bridges the gap between data at rest and real-time intelligence, ensuring that businesses can make informed decisions with up-to-the-moment data, without replacing their current systems.